A Method for Lightning Electromagnetic Signal Identification Using Cross-Layer Deep Feature Fusion

SONG Lin; YANG Jun; CAO Wei; ZHAO Ziqi; NING Yuan; WANG Wenjing; ZHANG Qilin

doi:10.11999/JEIT251134

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SONG Lin, YANG Jun, CAO Wei, ZHAO Ziqi, NING Yuan, WANG Wenjing, ZHANG Qilin. A Method for Lightning Electromagnetic Signal Identification Using Cross-Layer Deep Feature Fusion[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251134

Citation:

SONG Lin, YANG Jun, CAO Wei, ZHAO Ziqi, NING Yuan, WANG Wenjing, ZHANG Qilin. A Method for Lightning Electromagnetic Signal Identification Using Cross-Layer Deep Feature Fusion[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251134

Citation:

SONG Lin, YANG Jun, CAO Wei, ZHAO Ziqi, NING Yuan, WANG Wenjing, ZHANG Qilin. A Method for Lightning Electromagnetic Signal Identification Using Cross-Layer Deep Feature Fusion[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251134

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A Method for Lightning Electromagnetic Signal Identification Using Cross-Layer Deep Feature Fusion

doi: 10.11999/JEIT251134 cstr: 32379.14.JEIT251134

SONG Lin^{1, 2
,},
YANG Jun^{3, 4},
CAO Wei²,
ZHAO Ziqi^{3, 4},
NING Yuan^{3, 4},
WANG Wenjing^{3, 4},
ZHANG Qilin^{3, 4}

1.
Qingdao Ecology and Agricultural Meteorology Center, Qingdao 266003, China
2.
Key Opening Laboratory of Lightning Protection, China Meteorological Administration, Beijing 100081, China
3.
Key Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China
4.
International Joint Laboratory on Climate and Environmental Change, Nanjing University of Information Science and Technology, Nanjing 210044, China

Funds: The Natural Science Foundation of Shandong Province (ZR2023MD012), The Open Research Project of the Key Laboratory of Lightning, China Meteorological Administration (2024KELL-B013)

Received Date: 2025-10-29
Accepted Date: 2026-03-24
Rev Recd Date: 2026-03-24

Available Online: 2026-04-19

Abstract

Abstract

Objective Lightning identification is essential for lightning observation, location, warning, and disaster prevention. Large volumes of Low-Frequency/Very-Low-Frequency (LF/VLF) Lightning Electromagnetic Pulse (LEMP) waveform data require automatic and accurate classification methods. Deep learning has been widely used for feature extraction and classification, providing a feasible approach for LEMP waveform identification. However, anthropogenic electromagnetic interference and natural LEMP signals often overlap in the time and frequency domains. Their waveform features are also complex and diverse, which limits the accuracy and generalization ability of existing identification algorithms. Therefore, a more efficient deep learning model is required to distinguish LEMP signals from non-lightning electromagnetic signals. Methods This paper proposes a Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) deep neural network model that integrates multi-scale residual convolution and cross-layer feature fusion. The model is designed for binary classification of LEMP and non-lightning electromagnetic signals and enables accurate diagnostic identification of LEMP signals. Using observational data from an LF/VLF lightning magnetic-field detection system, a multi-scale residual network is first used to extract multidimensional features from electromagnetic waveforms layer by layer. The time-domain features output by each convolutional layer are then organized into a cross-layer time-domain feature sequence according to network depth. This sequence is input into the LSTM module for adaptive weighted fusion. This mechanism uses the sequence modeling ability of LSTM to learn the relative importance of features at different hierarchical levels, rather than to model the temporal dynamics of the original waveform. Results and Discussions The proposed CNN-LSTM model achieves a precision of 100%, a recall of 99.82%, an F1-score of 99.91%, and an accuracy of 99.89%. It obtains the best performance across all evaluation metrics. The model effectively identifies LEMP samples and reduces the misclassification of non-lightning samples. The Bayes classifier achieves high precision (93.14%), but its recall is relatively low (80.14%). The Support Vector Machine (SVM) model improves on the Bayes classifier across all metrics, but it remains inferior to the proposed CNN-LSTM model. The Multilayer Perceptron (MLP) and K-Nearest Neighbor (KNN) models also show limitations in precision, recall, and accuracy compared with CNN-LSTM. The Decision Tree (DT) model obtains reasonable results, but its precision and recall are lower than those of MLP and KNN, with a recall of only 88.01%. These results indicate that CNN-LSTM has clear advantages in LEMP waveform identification. This improvement is mainly attributed to the multi-scale residual CNN module, which automatically extracts low-level local features from raw waveform data. Additionally, the LSTM-based adaptive weighted fusion mechanism is applied to feature sequences from different network layers. As a feature integration tool across network depths, its input is an inter-layer feature sequence rather than an original waveform time series. This design improves the flexibility and discriminative ability of feature fusion, enables the model to learn the relative importance of features at different network depths, and supports effective aggregation of discriminative features. A confusion matrix was also generated to evaluate classification performance on the test set. Overall, comparison with baseline models confirms the superiority of the proposed model for LEMP waveform identification. Conclusions The CNN-LSTM model effectively identifies LEMP samples and reduces the misclassification of non-lightning samples. Compared with baseline models, it shows excellent identification performance in the binary classification of LEMP and non-lightning electromagnetic signals. The results also verify the effectiveness of convolutional feature extraction and LSTM-based cross-layer feature fusion for LEMP waveform identification.
- Lightning ElectroMagnetic Pulse (LEMP) signal,
- Deep learning,
- Convolutional Neural Network (CNN),
- Long Short-Term Memory (LSTM) Network,
- Feature fusion

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References(23)

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